[Bayesian Optimization]Kriging Believer and Constant Liar - Code World

[Bayesian Optimization]Kriging Believer and Constant Liar

Enterprise 2023-04-09 22:42:11 views: null

Both KB and CL are improvements based on the q-EI method. is an approximate parallel strategy. Please point out if there is something wrong!

Ordinary q-EI:

Remember the black box function $y(x)$

Select q points in each round of iterations, and calculate the true response value of each point, that is $\left \{x^{n+1},...,x^{N+q} \right \},\left \{y(x^{n+1}),...,y(x^{n+q}) \right \}$

KB:

Remember the proxy model established by the current known data $\tilde{Y}(x)$

Select q points in each iteration, and each point is no longer the real response value, but the value of the proxy model, that is, $\left \{x^{n+1},...,x^{N+q} \right \},\left \{\tilde{Y}(x^{n+1}),...,\tilde{Y}(x^{n+q}) \right \}$

CL:

Remember the known points $Y$ , the current constant is L, where L can be $min(Y)$ , $mean(Y)$ , $max(Y)$

Select q points in each iteration, and each point is no longer the real response value, but the value of the proxy model, that is,

$\left \{x^{n+1},...,x^{N+q} \right \},\left \{L,...L \right \}$

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Origin blog.csdn.net/qq_44785318/article/details/123644298

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